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Bayesian methods of inference are deeply natural and extremely powerful. Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice, freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib.
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243021207
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
85% of respondents would recommend this to a friend
Item #:
243021207
SAR 217
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Bayesian methods of inference are deeply natural and extremely powerful. Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice, freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib.
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What Stands Out
Hands-On Approach
Utilizes practical coding examples to demystify Bayesian concepts, making it accessible to hackers and programmers looking to apply statistical methods in real-world scenarios.
Comprehensive Coverage
Covers both fundamental and advanced topics in probabilistic programming and Bayesian inference, catering to both beginners and experienced users seeking to enhance their statistical toolkit.
Community Focused
Promotes collaboration and discussion within the data science community, aiding readers in problem-solving and sharing insights to foster a deeper understanding of Bayesian methodologies.
Product Details
| Country of Origin | This item will be imported from UK |
| Item Weight | 1.5 lbs (680 grams) |
| What is in the box | Bayesian Methods for Hackers:... For more details, please check description/product details |
Who Should Buy?
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Data Scientists
Those looking to deepen their understanding of Bayesian methods in data analysis and predictive modeling will benefit significantly.
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Statistical Programmers
Users familiar with Python who want to apply Bayesian techniques through practical coding examples will find it invaluable.
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Graduate Students
Students in statistics or data science seeking a hands-on introduction to probabilistic programming will gain a solid foundation.
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Absolute Beginners
Individuals with no background in statistics or programming may find the material too complex and challenging to comprehend.
Product Description
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
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Data Mining Editorial Review
Bayesian Methods For Hackers: Probabilistic Programming And Bayesian Inference (Addison-Wesley Data & Analytics) offers a comprehensive introduction to Bayesian inference, adeptly using the pymc library to simplify complex concepts with practical examples. The book's accessible chatty writing style makes understanding math enjoyable, and it encourages readers to experiment with code on their own. Although some readers noted discrepancies with library versions, many found it easy to adapt the examples to their Python environment. This 250-page volume, published by Addison-Wesley Professional, is ideal for those keen on enhancing their statistical programming skills, especially in Python.
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Pros
- Accessible writing style makes complex concepts easy to grasp
- Provides practical examples to reinforce learning
- Encourages experimentation with code and real-world applications
- Detailed exploration of Bayesian methods for learners
- Great resources for users familiar with Python
Cons
- Content is available freely online, which may influence buyers.
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Features & Benefits
- Deeply natural and extremely powerful Bayesian methods of inference
- Learn Bayesian inference from a computational perspective using PyMC
- Illuminates Bayesian inference through probabilistic programming with NumPy, SciPy, and Matplotlib
- Building and debugging models, testing model's “goodness of fit”
- Overcoming exploration versus exploitation dilemma, using Bayesian Inference to improve A/B testing
- Solving data science problems with small amounts of data
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